Background There is an increasing body of evidence from trials suggesting that major reductions in neonatal mortality are possible through community-based interventions. Since these trials involve packages of varying content, determining how much of the observed mortality reduction is due to specific interventions is problematic. The Lives Saved Tool (LiST) is designed to facilitate programmatic prioritization by modelling mortality reductions related to increasing coverage of specific interventions which may be combined into packages.

Methods To assess the validity of LiST outputs, we compared predictions generated by LiST with observed neonatal mortality reductions in trials of packages which met inclusion criteria but were not used as evidence inputs for LiST.

Results Four trials, all from South Asia, met the inclusion criteria. The neonatal mortality rate (NMR) predicted by LiST matched the observed rate very closely in two effectiveness-type trials. LiST predicted NMR reduction was close (absolute difference <5/1000 live births) in a third study. The NMR at the end of the fourth study (Shivgarh, India) was overestimated by 39% or 16/1000 live births.

Conclusions These results suggest that LiST is a reasonably reliable tool for use by policymakers to prioritize interventions to reduce neonatal deaths, at least in South Asia and where empirical data are unavailable. Reasons for the underestimated reduction in one trial likely include the inability of LiST to model all effective interventions.

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Mission

LiST aims to improve the quality of information available to support public health policies and decision making in maternal and child health and nutrition. The focus is on low-income countries, and on research that produces information to improve programs and policies. IIP is a network of scientists based in various institutions, and is housed administratively within the Johns Hopkins Bloomberg School of Public Health.Get more Information